scholarly journals Long-arm functional individuation of computation

Synthese ◽  
2021 ◽  
Author(s):  
Nir Fresco
Keyword(s):  
Environmental Factors ◽  
Functional Properties ◽  
Physical Process ◽  
Physical System ◽  
Environment Interaction ◽  
Mathematical Functions ◽  
System Environment ◽  
Functional Strategy ◽  
Semantic Properties

AbstractA single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Gene–gene and gene-environment interactions on cord blood total IgE in Chinese Han children

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Li Hua ◽  
Quanhua Liu ◽  
Jing Li ◽  
Xianbo Zuo ◽  
Qian Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Cord Blood ◽  
Synergistic Effects ◽  
Gene Interaction ◽  
Independent Effect ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Background IL13, IL4, IL4RA, FCER1B and ADRB2 are susceptible genes of asthma and atopy. Our previous study has found gene–gene interactions on asthma between these genes in Chinese Han children. Whether the interactions begin in fetal stage, and whether these genes interact with prenatal environment to enhance cord blood IgE (CBIgE) levels and then cause subsequent allergic diseases have yet to be determined. This study aimed to determine whether there are gene–gene and gene-environment interactions on CBIgE elevation among the aforementioned five genes and prenatal environmental factors in Chinese Han population. Methods 989 cord blood samples from a Chinese birth cohort were genotyped for nine single-nucleotide polymorphisms (SNPs) in the five genes, and measured for CBIgE levels. Prenatal environmental factors were collected using a questionnaire. Gene–gene and gene-environment interactions were analyzed with generalized multifactor dimensionality methods. Results A four-way gene–gene interaction model (IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713) was regarded as the optimal one for CBIgE elevation (testing balanced accuracy = 0.5805, P = 9.03 × 10–4). Among the four SNPs, only IL13 rs20541 was identified to have an independent effect on elevated CBIgE (odds ratio (OR) = 1.36, P = 3.57 × 10–3), while the other three had small but synergistic effects. Carriers of IL13 rs20541 TT, IL13 rs1800925 CT/TT, IL4 rs2243250 TT and ADRB2 rs1042713 AA were estimated to be at more than fourfold higher risk for CBIgE elevation (OR = 4.14, P = 2.69 × 10–2). Gene-environment interaction on elevated CBIgE was found between IL4 rs2243250 and maternal atopy (OR = 1.41, P = 2.65 × 10–2). Conclusions Gene–gene interaction between IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713, and gene-environment interaction between IL4 rs2243250 and maternal atopy begin in prenatal stage to augment IgE production in Chinese Han children.

Gene–environment interplay in the etiology of psychosis

2018 ◽  
Vol 48 (12) ◽  
pp. 1925-1936 ◽  
Author(s):  
Alyson Zwicker ◽  
Eileen M. Denovan-Wright ◽  
Rudolf Uher
Keyword(s):  
Environmental Factors ◽  
Genetic Risk ◽  
Environmental Exposures ◽  
Response To Treatment ◽  
Human Potential ◽  
Specific Gene ◽  
Environment Interaction ◽  
Gene Environment ◽  
Increased Risk ◽  
The Impact

AbstractSchizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene–environment interactions. Only a few specific gene–environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene–environment interaction is a common polymorphism in theAKT1gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.

scholarly journals Photonic simulation of system-environment interaction: Non-Markovian processes and dynamical decoupling

2013 ◽  
Vol 88 (6) ◽  
Author(s):  
Chang-Ling Zou ◽  
Xiang-Dong Chen ◽  
Xiao Xiong ◽  
Fang-Wen Sun ◽  
Xu-Bo Zou ◽  
...  
Keyword(s):  
Environment Interaction ◽  
Dynamical Decoupling ◽  
System Environment ◽  
Markovian Processes

scholarly journals Efficient simulation of non-Markovian system-environment interaction

2016 ◽  
Vol 18 (2) ◽  
pp. 023035 ◽  
Author(s):  
Robert Rosenbach ◽  
Javier Cerrillo ◽  
Susana F Huelga ◽  
Jianshu Cao ◽  
Martin B Plenio
Keyword(s):  
Environment Interaction ◽  
System Environment ◽  
Efficient Simulation ◽  
Markovian System

scholarly journals Rural environments reduce the genetic influence on adolescent substance use and rule-breaking behavior

2007 ◽  
Vol 38 (9) ◽  
pp. 1341-1350 ◽  
Author(s):  
L. N. Legrand ◽  
M. Keyes ◽  
M. McGue ◽  
W. G. Iacono ◽  
R. F. Krueger
Keyword(s):  
Substance Use ◽  
Environmental Factors ◽  
Externalizing Behavior ◽  
Genetic Influence ◽  
Adolescent Substance Use ◽  
Environment Interaction ◽  
Adolescent Substance ◽  
Oppositional Defiant ◽  
Rural Environments ◽  
Rule Breaking

BackgroundThere is increasing evidence that certain environmental factors can modify genetic effects. This is an important area of investigation as such work will help to guide the development of new intervention programs. In this paper, we address whether rural environments moderate the genetic influence on adolescent substance use and rule-breaking behavior (i.e. externalizing psychopathology).MethodOver 1200 Minnesotan 17-year-old twins were classified as either urban or rural. Externalizing behavior was operationalized as the use and abuse of alcohol and drugs along with symptoms of conduct, oppositional defiant, and antisocial personality disorders. Biometric factor modeling estimated whether the relative contribution of genetic and shared environmental factors varied from urban to rural settings.ResultsResidency effects reached statistical significance in the male sample only. In urban environments, externalizing behavior was substantially influenced by genetic factors, but in rural environments, shared environmental factors became more influential. This was apparent at both the individual-variable and factor levels.ConclusionsThese findings suggest a gene–environment interaction in the development of male adolescents' problem behaviors, including substance use. The results fit within an expanding literature demonstrating both the contextual nature of the heritability statistic and how certain environments may constrain the expression of genetic tendencies.

scholarly journals Decoherence and pointer states in small antiferromagnets: A benchmark test

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Hylke Donker ◽  
Hans De Raedt ◽  
Mikhail Katsnelson
Keyword(s):  
Quantum Measurement ◽  
The Self ◽  
Environment Interaction ◽  
Benchmark Test ◽  
System Environment ◽  
Preferred Basis ◽  
Measurement Limit ◽  
Pointer States ◽  
Decoherence Process

We study the decoherence process of a four spin-1/2 antiferromagnet that is coupled to an environment of spin-1/2 particles. The preferred basis of the antiferromagnet is discussed in two limiting cases and we identify two exact pointer states. Decoherence near the two limits is examined whereby entropy is used to quantify the robustness of states against environmental coupling. We find that close to the quantum measurement limit, the self-Hamiltonian of the system of interest can become dynamically relevant on macroscopic timescales. We illustrate this point by explicitly constructing a state that is more robust than (generic) states diagonal in the system-environment interaction Hamiltonian.

scholarly journals Irreversible Physical Process and Information Height

2021 ◽  
Author(s):  
Qin Liu ◽  
Quan Tang ◽  
Yuling Yi ◽  
Yu Feng
Keyword(s):  
Physical Process ◽  
Physical System ◽  
High Probability ◽  
Physical Phenomenon ◽  
Small Probability ◽  
Simplified Models ◽  
Extra Information ◽  
High Information

The time&rsquo;s arrow of macroscopic physical phenomenon is reflected by irreversible physical process, which essentially occurs from small probability state to high probability state. In this paper, simplified models are proposed to understand the macroscopic physical process. In order to describe the information of a physical system, we defined the full self-information as "information height" to describe the complexity or difficulty of a macrostate of physical system. In this way, the direction of macroscopic physical process is from high information height to low information height. We can judge the direction of physical process by the information height. If we want the macroscopic physical process to evolve from the low information height state to the high information height state, the system need to add extra information and corresponding energy to increase the information height.

scholarly journals Environmental and Genetic Regulation of Plant Height in Soybean

2020 ◽  
Author(s):  
Qing Yang ◽  
Gaoming Lin ◽  
Huiyong Lv ◽  
Cunhu Wang ◽  
Yongqing Yang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Soil Properties ◽  
Plant Height ◽  
Meteorological Factors ◽  
Day Length ◽  
Maximum Temperature ◽  
Soybean Plant ◽  
Environment Interaction ◽  
Shoot Architecture ◽  
Average Maximum

Abstract Background: Shoot architecture is fundamentally crucial to crop growth and productivity. As a key component of shoot architecture, plant height is known to be controlled by both genetic and environmental factors, though specific details remain scarce.Results: In this study, 308 representative soybean lines from a core collection and 168 F9 soybean progeny were planted at distinct field sites. The results demonstrated the presence of significant genotype × environment interaction (G × E) effects on traits associated with plant height in a natural soybean population. In total, 19 loci containing 51 QTLs (quantitative trait locus) for plant height were identified across four environments, with 23, 13 and 15 being QTLs for SH (shoot height), SNN (stem node number) and AIL (average internode length), respectively. Significant LOD ranging from 2.50 to 16.46 explained 2.80% - 26.10% of phenotypic variation. Intriguingly, only two loci, Loc11 and Loc19-1, containing 20 QTLs, were simultaneously detected across all environments. Results from Pearson correlation analysis and PCA (principal component analysis) revealed that each of the five agro-meteorological factors and four soil properties significantly affected soybean plant height traits, and that the corresponding QTLs had additive effects. Among significant environmental factors, AD (average day-length), AMaT (average maximum temperature), pH, and AN (available nitrogen) had the largest impacts on soybean plant height. Therefore, in spite of uncontrollable agro-meteorological factors, soybean shoot architecture might be remolded through combined efforts to produce superior soybean genetic materials while also optimizing soil properties.Conclusions: Overall, the comprehensive set of relationships outlined herein among environment factors, soybean genotypes and QTLs in effects on plant height opens new avenues to explore in work aiming to increase soybean yield through improvements in shoot architecture.

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